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Parametric vs Non-Parametric Algorithms (Non-Parametric (Advantages…
Parametric vs Non-Parametric
Algorithms
Non-Parametric
Become more complex
with data increases
No parameter
Do not make strong
assumptions
about the data
Has
flexible
number of
parameters
Generalize to unseen data
Advantages
Flexibility
Capable of fitting large number of functional form
Power
No/weak assumptions
Performance
Can result in high performance
Disadvantages
Required
a lot
of training data
Slow in training
Overfitting
Learning
Function
Can be summarized as learning a function f
such that
Y=f(x)
that map input x to output Y
Different algorithms make different
assumptions
or
biases
Parametric
Algorithms that simplify the function
Make
strong assumption
on data
Has a
fixed number
of parameters
2 Steps:
Select a form for the
function
Learn the
coefficients
for the function
from the training data
Benefit
Simple
Easy to understand & interpret result
Speed
Very fast to learn from data
Less data
Do not required much training data
Limitation
Constrained
Highly constrained to the
specified form
Limited Complexity
Suit for simple problem
Poor Fit
Unlikely
to match
the underlying mapping function